Indian Institute of Technology Bombay

About: Indian Institute of Technology Bombay is a education organization based out in Mumbai, India. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 16756 authors who have published 33588 publications receiving 570559 citations.

Censor networks: a critique of "sensor networks" from a systems perspective

Abstract: This writeup presents a critique of the field of "Wireless Sensor Networks (WSNs)". Literature in this domain falls into two main, distinct categories: (1) algorithms or protocols, and (2) applicationcentric system design. A striking observation is that references across these two categories are minimal, and superficial at best. We argue that this is not accidental, and is the result of three main flaws in the former category of work. Going forward, an applicationdriven, bottom-up approach is required for meaningful articulation and subsequent solution of any networking issues in WSNs.

Recent development in transition metal-catalysed C–H olefination

Abstract: Transition metal-catalysed functionalizations of inert C–H bonds to construct C–C bonds represent an ideal route in the synthesis of valuable organic molecules. Fine tuning of directing groups, catalysts and ligands has played a crucial role in selective C–H bond (sp2 or sp3) activation. Recent developments in these areas have assured a high level of regioselectivity in C–H olefination reactions. In this review, we have summarized the recent progress in the oxidative olefination of sp2 and sp3 C–H bonds with special emphasis on distal, atroposelective, non-directed sp2 and directed sp3 C–H olefination. The scope, limitation, and mechanism of various transition metal-catalysed olefination reactions have been described briefly.

A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks

Abstract: The engineering properties of rocks play a significant role in planning and designing of mining and civil engineering projects. A laboratory database of mechanical and engineering properties of rocks is always required for site characterization and mineral exploitation. Due to discontinuous and variable nature of rock masses, it is difficult to obtain all physicomechanical properties of rocks precisely. Prediction of unconfined compressive strength from seismic wave velocities (Compressional wave, Shear wave) and density of rock using generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference systems (ANFIS) can be appropriate and alternate methods to minimize the time and cost of tests. GRNN and ANFIS models were trained with 41 data sets using conjugate gradient descent algorithms and hybrid learning algorithm, respectively. Performance of both the models was examined with 15 testing data sets. In the present study, obtained network performance indices such as correlation coefficient, mean absolute percentage error, root mean square error and variance account for indicate high performance of predictive capability of GRNN system and closer to actual data over the ANFIS.

Modeling of a Single Resistance Capacitance Pulse Discharge in Micro-Electro Discharge Machining

Abstract: Micro-EDM (electro discharge machining) is a derived form of EDM process especially evolved for micro-machining. The use of resistance capacitance pulse generator, an advanced controller for machining in smaller interelectrode gaps and with lower discharge energies than in EDM, makes the material removal characteristics of a single discharge in micro-EDM different from that of the EDM. A comprehensive model predicting the material removal in a single discharge in micro-EDM is conceptualized. The model incorporates various phenomena in the prebreakdown period. It considers plasma as a time-variable source of energy to the cathode and anode to evaluate material removal at the electrodes. The plasma temperature and radius of the crater at the cathode (workpiece) predicted using the model were found to agree well with the experimental data in the literature.